Data and analytics are now key to business decision making, driving research, and automating human tasks. Many surveys rank Data Scientist as one of the best jobs in America and one of the highest-paid. This Data Science Workshop (DSW) at UKC aims to provide a deeper hands-on experience with a crash-course on data science, machine learning, and deep learning for those with little to no prior experience.
The 4-hour Data Science Workshop will be held virtually in the afternoon of Thursday, December 17, 2020, from 1 pm – 5 pm EST during UKC. The virtual interactive sessions will be run on a video-conferencing platform like Zoom. Attendees will run the code on the Google Colab online web browser environment using the Python programming language but prior programming experience is not necessary. The topics covered will include
- Introduction to Data Science and Machine Learning
- Basics of data cleaning, exploratory data analysis, and data visualization
- Reviewing machine learning models like random forests and neural networks
- Panelist discussion of various data science applications
- Special Topic Overview: Convolutional Neural Networks
- Introduction to state-of-the-art convolutional neural networks for images
- Hands-on exercise in Keras/Tensorflow for an image recognition classifier
The data science workshop registration is available within the UKC registration and requires a refundable $25 reservation fee that will be fully refunded after confirmed workshop attendance. No refund if absent. Any questions may be addressed to the organizers at firstname.lastname@example.org.
Instructors and Organizers:
Benjamin Lee (Chair, Instructor) – Senior Research Associate at Weill Cornell Medicine: Benjamin Lee is a Sr. Research Associate at Weill Cornell Medicine in New York, NY. Ben is a researcher developing machine learning algorithms for cardiac medical imaging focusing on convolutional neural networks for 3D SPECT/PET/CT images for disease detection. Ben received his Ph.D. at the University of Michigan in Electrical Engineering specializing in image processing and image reconstruction and his B.S. from Cornell University.
Ahreum Amy Han (Co-chair, Instructor) – Senior Modeler/Data Scientist at Discover Financial Services: Amy Han is a Data Scientist, and currently with Discover Financial Services and formally with IBM, Allstate, and UL. She is also an educator in passon. She is a formal lecturer at Southern Illinois University at Carbondale. Her recent work focuses on Natural Language Processing (NLP) and Text mining. She earned her M.S in Applied Mathematics and B.S. in Pure Mathematics.
DK Kim (Co-Chair, Instructor) – Senior Data Scientist at Discover Financial Services: DK Kim is currently a data scientist at Discover Financial Services in Chicago, IL. He received his BS in Industrial Engineering with minors in Statistics and Mechanical Engineering from Texas Tech. He is currently pursuing his master’s in Computer Science/Machine Learning at Georgia Tech part-time online. He worked as a data scientist at Edison Energy and the NPD Group prior to his current role at Discover. DK enjoyed traveling prior to COVID-19 and his hobby includes loyalty and frequent flyer programs.
June Park (Organizer) – Operations Data Analyst at Groundspeed Analytics, Inc.: June Park is a data analyst at an insuretech startup Groundspeed in Ann Arbor, Michigan. She currently focuses on training an offshore resource team. June received M.S. in Information at the University of Michigan and B.S. in Computational Media at Georgia Tech.
Jongmu Joshua Oh (Organizer) – Analytical Consulting Manager Formerly at Canada Post: Joshua is a business intelligence professional with a background in IT consulting and product management. Joshua received his MBA at the University of Toronto and B.S in Electrical Engineering at the University of Waterloo.Joshua’s latest work involves working in logistics & transportation analytics at Canada Post.
Jeho Park (Advisor) – Director of the Murty Sunak Quantitative and Computing Lab at Claremont McKenna College: Jeho Park is the founding Director of the Quantitative and Computing Lab at Claremont McKenna College. He leads the center to assist students and faculty with quantitative, statistical, and computational skills through tutoring, workshops, and consultations. He also teaches high-performance computing and data science courses. He received a Ph.D. in Engineering and Applied Mathematics/Computer Science from Claremont Graduate University. Dr. Park’s primary research and professional interests include Data Science, Data Analytics and Quantitative Methods, AI/Machine Learning, and High-Performance Computing.